Meng Jiang

Decentralized RAG allows your database to benefit all LLM clients. On the other side, not all data sources are reliable. Managing source reliability on blockchain can avoid third-party manipulation. Introducing dRAG + Blockchain + Truth Discovery: arxiv.org/abs/2511.07577

𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝟵.𝟭𝟭>𝟵.𝟵 𝗮𝗻𝗱 𝗮 𝘁𝗿𝗶𝗮𝗻𝗴𝗹𝗲 𝗵𝗮𝘀 𝗳𝗼𝘂𝗿 𝘀𝗶𝗱𝗲𝘀, 𝘁𝗵𝗲𝗿𝗲𝗳𝗼𝗿𝗲 𝟭+𝟭=𝟮. LLMs and Language Agents can sometimes generate correct answers from blatantly incorrect reasoning, which is more often in complex tasks, and exacerbated by reinforcement learning (RL), the commonly believed silver bullet to complex reasoning in LLMs. This is due to a well-known phenomenon called reward hacking, where if the only training signal LLMs are getting from the training data exclusively regards the final result, then LLMs are incentivized to match the correct final output through whatever means possible on its training data, leading to inconsistent and ungeneralizable reasoning processes in RL's wake. With our intern Mengzhao Jia, we (@ignaciocases and myself, plus folks from @Meng_CS s lab at Notre Dame) explore a simple fix: can we use the LLM's own reasoning to provide some additional supervision signal for the reasoning process itself, so that besides the final result, the LLM is also encouraged to stay consistent in its reasoning during training? We design an algorithm to automatically create rubrics for LLM reasoning processes, and train the model to adhere to these rubrics alongside generating correct final answers during RL. The resulting model not only produces significantly more consistent reasoning, but also generalizes better on a wide range of complex reasoning tasks we benchmarked, even with just 10% of the training data. We hope this technique helps pave the way to more powerful and generalizable reasoning models for complex tasks. Read more in our preprint: arxiv.org/pdf/2510.14738

Thrilled to share that “Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates” paper has been accepted to EMNLP 2025 (Main Conference)!🎉 📄 Check it out on arXiv: arxiv.org/abs/2509.18076 project page: hygiadang.com/publication/em… 1/3

.@DomSoos from @WebSciDL and @oducs is presenting "Can LLMs Beat Humans on Discerning Human-written and LLM-generated Science News?" They explored whether LLMs can outperform humans for LLM-generated vs. human written news. 🔗doi: 10.1145/3720553.3746674 #LLM #NLP @fanchyna

@NoahZiems I've co-directed it for 6 months!!! lucyinstitute.nd.edu/centers-and-la…

@Meng_CS We have a foundation models lab?!

Job opportunity (postdoc at Notre Dame Foundation Models Lab): apply.interfolio.com/173333

✴️ Pleased to introduce our new paper yining610.github.io/dynamic-reward… - Rebalance multiobjectives during training through dynamic reward weighting - Build Pareto-dominant front over static baselines across online RL algorithms, datasets, and model families - Faster convergence rate 1/8

Come be my colleague! @ND_CSE at @NotreDame is hiring a tenure-track professor in computer vision! (And robotics and quantum.) More info here: careercenter.cra.org/job/university…

@lateinteraction @NoahZiems @MIT_CSAIL @DSPyOSS Congratulations to both of you! Glad to see the "interaction"/collaboration is getting strong and soon fruitful - and never too "late" :) @lateinteraction I am so excited too! Way to go, wonderful @NoahZiems ! Change the world with mind and hand!

@NoahZiems @MIT_CSAIL @Meng_CS @DSPyOSS Welcome Noah!! So great to have you as a founding member here of this new lab :D And I’m so excited to continue to collaborate with and learn more closely from Meng!

Quick update! This year I am on visit at @MIT_CSAIL working under the wonderful @lateinteraction while I am continuing to be advised by the wonderful @Meng_CS Right now my focus is to continue making Arbor a fantastic RL framework for optimizing @DSPyOSS programs

🔥 Only 15 days left! 🔥 The Open Polymer Challenge already has 9,800+ entrants and 38,000+ submissions. If you have not joined yet, let’s jump in these last few days to 🌍 accelerate polymer discovery with ML and go for 💰 $50,000 in prizes. 👉 LINK: kaggle.com/competitions/n…

New paper: Reflective Prompt Evolution Can Outperform GRPO. It's becoming clear that learning via natural-language reflection (aka prompt optimization) will long be a central learning paradigm for building AI systems. Great work by @LakshyAAAgrawal and team on GEPA and SIMBA.

@msbernst Congratulations!

Thank you to everyone for your energy and enthusiasm in joining this adventure with me so far!

🎓💰🔬 Want to learn machine learning, win a cash prize (USD 50K in total!!), and help drive real progress in discovering new polymer materials? All available at our NeurIPS 2025 Open Polymer Challenge: open-polymer-challenge.github.io 🚀 Join now (Kaggle): kaggle.com/competitions/n… 📈

Open Polymer Challenge: Leveraging Machine Learning for Polymer Informatics was accepted to NeurIPS 2025 Competition Track and is now LAUNCHed on Kaggle! JOIN US AND WIN $50,000 Awards! YES, FOUR "0"s - it's $50,000! Soooo what are YOU waiting for???

Introducing🔥torch-molecule🔥: A single line of code for molecular property prediction, generation & representation learning: > 30 deep learning methods + models, sklearn-style. All available at: `pip install torch-molecule` Code: github.com/liugangcode/to…

Our team, with @Meng_CS, Yihan, and Eric, is actively building and enriching the library. We welcome feedback, suggestions, and contributions from the community. Please feel free to reach out with any feedback.

Exciting news at the @lucy_institute! The Foundation Models and Applications Lab has launched with co-directors @Meng_CS and @xiangliangzhang from @NotreDame's Department of Computer Science and Engineering. Learn more: lucyinstitute.nd.edu/news-events/20…

So many things in the run-up to DSPy 3. Here's a first, EXPERIMENTAL one: 🚨We're releasing dspy.GRPO, an online RL optimizer for DSPy programs Your DSPy code as-is can be dspy.GRPO'ed. Yes, even compound multi-module programs. Led by @NoahZiems @LakshyAAAgrawal @dilarafsoylu.

Quick reminder that our paper, Benchmarking Language Model Creativity: A Case Study on Code Generation, will be presented today! 📅 11AM-12:30PM, Fri, May 2 📍 Hall 3 📝 arxiv.org/abs/2407.09007 🎥 youtube.com/watch?v=v1cHyC…

Excited for our workshop at NAACL 2025! 🎉 We have 6 amazing speakers and 28 accepted papers —proceedings are now online! 📄 Check out the full schedule here 👉 knowledge-nlp.github.io/naacl2025/sche…